Why We Twitter: Understanding Microblogging Usage and Communities Akshay Java Xiaodan Song University of Maryland Baltimore County NEC Laboratories America 1000 Hilltop Circle 10080 N. Wolfe Road, SW3-350 Baltimore, MD 21250, USA Cupertino, CA 95014, USA [email protected] [email protected] Tim Finin Belle Tseng University of Maryland Baltimore County NEC Laboratories America 1000 Hilltop Circle 10080 N. Wolfe Road, SW3-350 Baltimore, MD 21250, USA Cupertino, CA 95014, USA fi[email protected] [email protected] ABSTRACT provided by several services including Twitter2,Jaiku3 and 4 Microblogging is a new form of communication in which more recently Pownce . These tools provide a light-weight, users can describe their current status in short posts dis- easy form of communication that enables users to broadcast tributed by instant messages, mobile phones, email or the and share information about their activities, opinions and Web. Twitter, a popular microblogging tool has seen a lot status. One of the popular microblogging platforms is Twit- of growth since it launched in October, 2006. In this paper, ter [29]. According to ComScore, within eight months of its we present our observations of the microblogging phenom- launch, Twitter had about 94,000 users as of April, 2007 [9]. ena by studying the topological and geographical properties Figure 1 shows a snapshot of the first author’s Twitter home- of Twitter’s social network. We find that people use mi- page. Updates or posts are made by succinctly describing croblogging to talk about their daily activities and to seek one’s current status within a limit of 140 characters. Top- or share information. Finally, we analyze the user intentions ics range from daily life to current events, news stories, and associated at a community level and show how users with other interests. IM tools including Gtalk, Yahoo and MSN similar intentions connect with each other. have features that allow users to share their current status with friends on their buddy lists. Microblogging tools facili- tate easily sharing status messages either publicly or within Categories and Subject Descriptors asocialnetwork. H.3.3 [Information Search and Retrieval]: Information Search and Retrieval - Information Filtering; J.4 [Computer Applications]: Social and Behavioral Sciences - Economics General Terms Social Network Analysis, User Intent, Microblogging, Social Media 1. INTRODUCTION Microblogging is a relatively new phenomenon defined as “a form of blogging that lets you write brief text updates (usu- ally less than 200 characters) about your life on the go and send them to friends and interested observers via text mes- saging, instant messaging (IM), email or the web.” 1.Itis 1http://en.wikipedia.org/wiki/Micro-blogging Permission to make digital or hard copies of all or part of this work for Figure 1: An example Twitter homepage with up- personal or classroom use is granted without fee provided that copies are dates talking about daily experiences and personal not made or distributed for profit or commercial advantage and that copies interests. bear this notice and the full citation on the first page. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific 2http://www.twitter.com permission and/or a fee. 3 http://www.jaiku.com Joint 9th WEBKDD and 1st SNA-KDD Workshop ’07 , August 12, 2007 , 4 San Jose, California , USA . Copyright 2007 ACM 1-59593-444-8...$5.00. http://www.pownce.com Compared to regular blogging, microblogging fulfills a need to understand the user intentions and community structure for an even faster mode of communication. By encourag- in microblogging. From our analysis, we find that the main ing shorter posts, it lowers users’ requirement of time and types of user intentions are: daily chatter, conversations, thought investment for content generation. This is also one sharing information and reporting news. Furthermore, users of its main differentiating factors from blogging in general. play different roles of information source, friends or informa- The second important difference is the frequency of update. tion seeker in different communities. On average, a prolific bloger may update her blog once ev- ery few days; on the other hand a microblogger may post The paper is organized as follows: in Section 2, we describe several updates in a single day. the dataset and some of the properties of the underlying social network of Twitter users. Section 3 provides an anal- With the recent popularity of Twitter and similar microblog- ysis of Twitter’s social network and its spread across geogra- ging systems, it is important to understand why and how phies. Next, in Section 4 we describe aggregate user behav- people use these tools. Understanding this will help us ior and community level user intentions. Section 5 provides evolve the microblogging idea and improve both microblog- a taxonomy of user intentions. Finally, we summarize our ging client and infrastructure software. We tackle this prob- findings and conclude with Section 6. lem by studying the microblogging phenomena and analyz- ing different types of user intentions in such systems. 2. DATASET DESCRIPTION Much of research in user intention detection has focused on Twitter is currently one of the most popular microblogging understanding the intent of a search queries. According to platforms. Users interact with this system by either using a Broder [5], the three main categories of search queries are Web interface, IM agent or sending SMS updates. Members navigational, informational and transactional. Understand- may choose to make their updates public or available only to ing the intention for a search query is very different from friends. If user’s profile is made public, her updates appear user intention for content creation. In a survey of bloggers, in a “public timeline” of recent updates. The dataset used Nardi et al. [26] describe different motivations for “why in this study was created by monitoring this public timeline we blog”. Their findings indicate that blogs are used as a for a period of two months starting from April 01, 2007 to tool to share daily experiences, opinions and commentary. May 30, 2007. A set of recent updates were fetched once Based on their interviews, they also describe how bloggers every 30 seconds. There are a total of 1,348,543 posts from form communities online that may support different social 76,177 distinct users in this collection. groups in real world. Lento et al. [21] examined the im- portance of social relationship in determining if users would Twitter allows a user, A, to “follow” updates from other remain active in a blogging tool called Wallop. A user’s re- members who are added as “friends”. An individual who is tention and interest in blogging could be predicted by the not a friend of user A but “follows” her updates is known as a “follower”. Thus friendships can either be reciprocated or comments received and continued relationship with other 5 active members of the community. Users who are invited by one-way. By using the Twitter developer API , we fetched people with whom they share pre-exiting social relationships the social network of all users. We construct a directed tend to stay longer and active in the network. Moreover, cer- graph G(V,E), where V represents a set of users and E tain communities were found to have a greater retention rate represents the set of “friend” relations. A directed edge e due to existence of such relationships. Mutual awareness in exists between two users u and v if user u declares v as a social network has been found effective in discovering com- a friend. There are a total of 87,897 distinct nodes with munities [23]. 829,053 friend relation between them. There are more nodes in this graph due to the fact that some users discovered In computational linguists, researchers have studied the prob- though the link structure do not have any posts during the lem of recognizing the communicative intentions that un- duration in which the data was collected. For each user, we derlie utterances in dialog systems and spoken language in- also obtained their profile information and mapped their terfaces. The foundations of this work go back to Austin location to a geographic coordinate, details of which are [2], Stawson [32] and Grice [14]. Grosz [15] and Allen [1] provided in the following section. carried out classic studies in analyzing the dialogues be- tween people and between people and computers in coopera- 3. MICROBLOGGING IN TWITTER tive task oriented environments. More recently, Matsubara This section describes some of the characteristic properties [24] has applied intention recognition to improve the per- of Twitter’s Social Network including it’s network topology formance of automobile-based spoken dialog system. While and geographical distribution. their work focusses on the analysis of ongoing dialogs be- tween two agents in a fairly well defined domain, studying user intention in Web-based systems requires looking at both 3.1 Growth of Twitter the content and link structure. Since Twitter provides a sequential user and post identifier, we can estimate the growth rate of Twitter. Figure 2 shows In this paper, we describe how users have adopted a spe- the growth rate for users and Figure 3 shows the growth rate cific microblogging platform, Twitter. Microblogging is rel- for posts in this collection. Since, we do not have access to atively nascent, and to the best of our knowledge, no large historical data, we can only observe